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Vibration Analysis of Transformer DC bias Caused by HVDC based on EMD Reconstruction
Xingmou Liu,Yongming Yang,Yichen Huang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
This paper proposes a new approach utilizing empirical mode decomposition (EMD) reconstruction to process vibration signals of a transformer under DC bias caused by high voltage direction transmission (HVDC), which is the potential cause of additional vibration and noise from transformer. Firstly, the Calculation Method is presented and a 3D model of transformer is simulated to analyze transformer deformation characteristic and the result indicate the main vibration is produced along axial direction of three core limbs. Vibration test system has been built and test points on the core and shell of transformer have been measured. Then, the signal reconstruction method for transformer vibration based on EMD is proposed. Through the EMD decomposition, the corrupted noise can be selectively reconstructed by the certain frequency IMFs and better vibration signals of transformer have been obtained. After EMD reconstruction, the vibrations are compared between transformer in normal work and with DC bias. When DC bias occurs, odd harmonics, vibration of core and shell, behave as a nonlinear increase and the even harmonics keep unchanged with DC current. Experiment results are provided to collaborate our theoretical analysis and to illustrate the effectiveness of the proposed EMD method.
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Vibration Analysis of Transformer DC bias Caused by HVDC based on EMD Reconstruction
Liu, Xingmou,Yang, Yongming,Huang, Yichen The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
This paper proposes a new approach utilizing empirical mode decomposition (EMD) reconstruction to process vibration signals of a transformer under DC bias caused by high voltage direction transmission (HVDC), which is the potential cause of additional vibration and noise from transformer. Firstly, the Calculation Method is presented and a 3D model of transformer is simulated to analyze transformer deformation characteristic and the result indicate the main vibration is produced along axial direction of three core limbs. Vibration test system has been built and test points on the core and shell of transformer have been measured. Then, the signal reconstruction method for transformer vibration based on EMD is proposed. Through the EMD decomposition, the corrupted noise can be selectively reconstructed by the certain frequency IMFs and better vibration signals of transformer have been obtained. After EMD reconstruction, the vibrations are compared between transformer in normal work and with DC bias. When DC bias occurs, odd harmonics, vibration of core and shell, behave as a nonlinear increase and the even harmonics keep unchanged with DC current. Experiment results are provided to collaborate our theoretical analysis and to illustrate the effectiveness of the proposed EMD method.
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Liu Xingmou,Tian Hao,Wang Yan,Jiang Fan,Zhang Chenyang 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.6
With the widespread application of power inspections, the problem of insulator segmentation in complex environments has become a current challenge. An insulator image segmentation method based on adaptive region growing and the adaptive Otsu algorithm is proposed. The 8 neighborhood pixels are used for region growth, and the segmentation results are obtained through morphological processing. Finally, the original segmented image, dynamic threshold segmentation, global threshold segmentation, and adaptive region growth are quantitatively analyzed. For the result of natural lighting image segmentation, the accuracy of adaptive region growth segmentation is improved by 14.23% for the original segmentation. For the results of infrared image segmentation, the accuracy of adaptive region growing segmentation is improved by 8.13% compared with the original segmentation. Experimental results show that adaptive region growth threshold segmentation can extract contour information more completely, which has certain advantages compared with traditional threshold segmentation. It provides an important basis for the study of insulator fault diagnosis and infrared insulator temperature fi eld feature extraction.
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Zhen Shaoming,Liu Chenliang,Liu Xingmou 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.2
This paper presents a time–frequency analysis of the vibration of transformer under direct current (DC) bias through Hilbert– Huang transform (HHT). First, the empirical mode decomposition (EMD) process, which is the key in HHT, was introduced. The results of EMD, namely, intrinsic mode functions (IMFs), were calculated and summed by Hilbert transform (HT) to obtain time-dependent series in a 2D time–frequency domain. Next, the theory of DC bias for the transformer was analyzed. In consideration of the DC bias eff ect and in combination with the existing transformer vibration-related mechanism, the electromagnetic force equations of the transformer core were deduced. Lastly, a test system of vibration measurement for the transformer was set up. Three direction (x, y, and z axes) components of core vibration were measured. Decomposition of EMD and HHT spectra showed that vibration strength increased, and odd harmonics were produced with DC bias. This method illustrates the most obvious vibration distortion in the z-axis direction when the transformer is DC biased. Among them, the distortion of IMF3 has increased by more than 5 times. However, the distortion in the x-axis and y-axis directions also exists, but it is not obvious. Especially, 50 Hz component appeared in z-direction, 50 Hz component increased twofold in y-direction, and 150 Hz component increased threefold in z-direction. Results indicated that HHT can not only provide the occurrence time of DC bias but can also obtain the signal change components of the transformer vibration. Thus, HHT is a viable signal processing tool for transformer health monitoring.
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